Brain tumor detection from MRI images is crucial for early diagnosis and treatment. Various clustering algorithms, such as Fuzzy K-means (FKM), C-means (FCM), Self-Organizing Maps (SOM), have been used segmentation, but they face challenges like noise varying image intensities. This study evaluates the performance of Adaptive Moving Map (AMSOM) segmentation in images, comparing it to other methods. We evaluated FCM, FKM, SOM-FKM, AMSOM on a dataset 12 images. Performance was measured using...

Lung cancer is one of the leading causes death worldwide. Increasing patient survival rates requires early detection. Traditional methods diagnosis often result in late-stage detection, necessitating development more advanced and accurate predictive models. This paper has proposed a methodology for lung prediction using machine learning Synthetic minority over-sampling technique (SMOTE) used before classification to resolve problem class imbalance. Bayesian optimization enhance model’s...

Deep learning techniques have become vital in many fields the modern era because they are excellent at analysing and predicting real big data to act different situations. Although it is marvellous aspects, prone misinterpretation of data, so teams experienced specialists cannot be dispensed with following up on execution stages analysis. Convolutional Neural Network one most significant deep techniques. It widely employed visual image In this article, R-CNN Fast summarised compared best This...

Github is a very popular collaborative software-development platform that provides typical source-code management and issue tracking features augmented by strong social-networking such as following developers watching projects. These help ``spread the word'' about individuals projects, building reputation of former increasing popularity latter. In this paper, we investigate relation between project regular, consistent documentation updates. We found indicators consistently projects exhibited...

Bug deduplication, ie, recognizing bug reports that refer to the same problem, is a challenging task in software‐engineering life cycle. Researchers have proposed several methods primarily relying on information‐retrieval techniques. Our work motivated by intuition domain knowledge can provide relevant context enhance effectiveness, attempts improve use of information retrieval augmenting with knowledge. In our previous work, we software‐literature‐context method for using literature as...

Swaraj Khadanga, Karan Aggarwal, Shafiq Joty, Jaideep Srivastava. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint (EMNLP-IJCNLP). 2019.

Change-impact analysis, namely "identifying the potential consequences of a change" is an important and well studied problem in software evolution. Any change may potentially affect application's behaviour, performance, energy consumption profile. Our previous work demonstrated that changes to system-call profile application correlated with energy-consumption This paper evaluates describes GreenAdvisor, first its kind tool systematically records analyzes system calls predict whether has...

The automated identification of toxicity in texts is a crucial area text analysis since the social media world replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which inaccurate classification toxic words context. In this paper, several approaches for locating are assessed presented aiming enhance overall quality classification. General unsupervised methods were used...

In previous work by Alipour et al., a methodology was proposed for detecting duplicate bug reports comparing the textual content of to subject-specific contextual material, namely lists software-engineering terms, such as non-functional requirements and architecture keywords. When report contains word in these word-list contexts, is considered be associated with that context this information tends improve bug-deduplication methods. paper, we propose method partially automate extraction from...

One of the key challenges in predictive maintenance is to predict impending downtime an equipment with a reasonable prediction horizon so that countermeasures can be put place. Classically, this problem has been posed two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as long-term task estimate how much time left and (2) Failure (FP) short-term assess probability failure within pre-specified window. As these tasks related, performing them...

In the modern era, artificial intelligence applications have become one of most essential and prominent aspirations countries in their various organisations sectors, especially education sector, due to ability these techniques help this sector develop rapidly increase productivity by imparting scientific material a beautiful way learners. This article provides an overview significance role learning, how they can be employed future. All information scenario is collected from set studies...

Today, the science of artificial intelligence has become one most important sciences in creating intelligent computer programs that simulate human mind. The goal medical field is to assist doctors and health care workers diagnosing diseases clinical treatment, reducing rate error, saving lives citizens. main widely used technologies are expert systems, machine learning big data. In article, a brief overview three mentioned techniques will be provided make it easier for readers understand...

Abstract Cirrhosis is a liver disease that considered to be among the most common diseases in healthcare. Due its non-invasive nature, ultrasound (US) imaging widely accepted technology for diagnosis of this disease. This research work proposed method discriminating cirrhotic from normal through US images. The images were obtained radiologist. radiologist also specified region interest (ROI) these images, and then was applied it. Two parameters extracted differences intensity neighboring...